DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) based on an application filed in REPUBLIC OF INDIA on March 29, 2022. The certified copy has been filed in parent Application No. 18/127,313, filed on March 28, 2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-11 and 16-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Regarding claim 1:
Step 1 (whether a claim is to a statutory category):
Yes, the claim is within the four statutory categories (a process, machine, manufacture or composition of matter). Claim 1 recites an apparatus, therefore, falls within a manufacture category.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “providing information related to an intended federated learning training to a network function, wherein the intended federated learning training involves a set of candidate members each performing a respective local training;” wherein providing information is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation). See MPEP § 2106.04(a)(2)(III). And, “estimating a first expected quality of the intended federated learning training based on the first federated learning specific assistance information; and” wherein estimating quality is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation). See MPEP § 2106.04(a)(2)(III). And, “deciding whether or not to start the intended federated learning training based on the first expected quality of the intended federated learning training” wherein making a decision is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “one or more processors, and at least one memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform:” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “receiving, in response to the providing, first federated learning specific assistance information from the network function;” is an additional element that amounts to adding insignificant extra-solution activity (i.e., mere data gathering) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, the claims is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
Therefore, claim 1 is ineligible.
Regarding claim 2:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “wherein at least one of the following is valid: the network function is a stand-alone network function dedicated to assisting the federated learning training; the network function is comprised by a network exposure function; and the network function is comprised by a network data analytics function” is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
Therefore, claim 2 is ineligible.
Regarding claim 3:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “wherein the information related to the intended federated learning training comprises at least one of: the candidate members ;a size of an aggregated model of the intended federated learning training; a size of a local model of at least one of the local trainings; an expected number of iterations to be performed by each of the candidate members when performing the respective local training; for each of the candidate members: a time interval between each of the iterations to be performed by the respective candidate member; and an identifier of a network slice supporting the apparatus” is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 3 is ineligible.
Regarding claim 4:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein the first federated learning specific assistance information comprises at least one of for each of the candidate members of the set of the candidate members: an expected latency for performing at least one iteration of the local training by the respective candidate member;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “for each of the candidate members of the set of the candidate members: an expected average latency for performing plural iterations of the local training by the respective candidate member;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “for each of the candidate members of the set of the candidate members: an expected average latency for providing a result of an iteration of the local training to the apparatus;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “a suggested time window for performing the federated learning training; and a geographical distribution of the candidate members” is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 4 is ineligible.
Regarding claim 5:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform at least one of: starting the intended federated learning training if it is decided to start the intended federated learning training; and inhibiting the starting the intended federated learning training if it is decided not to start the intended federated learning training” is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 5 is ineligible.
Regarding claim 6:
Further modifies the abstract idea of claim 5.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “estimating a second expected quality of the intended federated learning training based on the second federated learning specific assistance information;” wherein estimating quality is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation). See MPEP § 2106.04(a)(2)(III). And, “deciding whether or not to start the intended federated learning training based on the second expected quality of the intended federated learning training” wherein making a decision is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform if it is decided not to start the intended federated learning training: requesting second federated learning specific assistance information for the intended federated learning training under the assumption that at least one further candidate member not belonging to the set of candidate members performs the respective local training;” is an additional element that amounts to adding insignificant extra-solution activity (i.e., mere data gathering) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). And, “receiving, in response to the providing, the second federated learning specific assistance information from the network function;” is an additional element that amounts to adding insignificant extra-solution activity (i.e., mere data gathering) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). And, “starting the intended federated learning training if it is decided to start the intended federated learning training” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 6 is ineligible.
Regarding claim 7 and analogous claim 16:
Step 1 (whether a claim is to a statutory category):
Yes, the claim is within the four statutory categories (a process, machine, manufacture or composition of matter). Claim 7 recites an apparatus, therefore, falls within a manufacture category. Claim 16 recites a method, therefore, falls within a process category.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “generating federated learning specific assistance information for the intended federated learning training based on the information related to the intended federated learning training;” wherein generate information is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation). See MPEP § 2106.04(a)(2)(III). And, “providing the federated learning specific assistance information in response to the receiving the information related to the intended federated learning training” wherein providing information is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “receiving information related to an intended federated learning training, wherein the intended federated learning training involves a set of candidate members performing a respective local training;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 7, 16 is/are ineligible.
Regarding claim 8 and analogous claim 17:
Further modifies the abstract idea of claim 7.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “for each of the candidate members, checking whether there is a consent to perform the respective local training; for each of the candidate members, obtaining a current location of the respective candidate member;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “for each of the candidate members, estimating a future location of the respective candidate member for the time when the respective local training is to be performed; for each of the candidate members, a respective data capacity for performing the respective local training and/or for transmitting a result of the respective local training;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “for each of the candidate members, estimating a radio link quality at the current location of the respective candidate member and/or at the future location of the respective candidate member;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “for each of the candidate members, checking if a slice supports the current location of the respective candidate member and/or the future location of the respective candidate member, wherein the information related to the intended federated learning training comprises an identifier of the slice; and for each of the candidate members, checking if the slice supports the respective candidate member” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 8, 17 is/are ineligible
Regarding claim 9 and analogous claim 18:
Further modifies the abstract idea of claim 7.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein the information related to the intended federated learning training comprises at least one of: the candidate members of the set of candidate members; a size of an aggregated model of the intended federated learning training; a size of a local model of at least one of the local trainings; an expected number of iterations to be performed by each of the candidate members when performing the respective local training; for each of the candidate members: a time interval between each of the iterations to be performed by the respective candidate member; and an identifier of a network slice” is/are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 9, 18 is/are ineligible.
Regarding claim 10 and analogous claim 19:
Further modifies the abstract idea of claim 7.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein the federated learning specific assistance information comprises at least one of for each of the candidate members of the set of the candidate members: an expected latency for performing at least one iteration of the local training by the respective candidate member;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “for each of the candidate members of the set of the candidate members: an expected average latency for performing plural iterations of the local training by the respective candidate member; for each of the candidate members of the set of the candidate members: an expected average latency for providing a result of an iteration of the local training to the apparatus; is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “a suggested time window for performing the federated learning training; and a geographical distribution of the candidate members” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 10, 19 is/are ineligible.
Regarding claim 11 and analogous claim 20:
Further modifies the abstract idea of claim 7
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein at least one of the apparatus is comprised in a stand-alone network function dedicated to assisting the federated learning training; and the apparatus is comprised in a network data analytics function” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B (Inventive concept):
No, the claims do not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Therefore, claim 11, 20 is/are ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang et al., Non-Patent Literature (“Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases”).
Regarding Claim 1:
Huang teaches:
An apparatus, comprising: one or more processors, and at least one memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform: providing information related to an intended federated learning training to a network function, wherein the intended federated learning training involves a set of candidate members each performing a respective local training; (Section III, A, paragraph 1, “As illustrated in Fig. 1, we consider a mobile edge computing (MEC) environment, which is built on metropolitan cellular networks. The synchronous-fashioned computing architecture consists of a central FL parameter server, a group of base stations [network function], and a large number of mobile devices with heterogeneous resources. The central FL parameter server initializes a global model for a specific computing task [intended federated learning] (i.e., wherein computing task under the broadest reasonable interpretation (BRI) is an intended task (i.e., FL training)) such as image classification (i.e., wherein the ‘providing information’ is given for a specific computing task). For each training task, the FL server recruits a group of mobile users (MUs) to join in a training task. The selected MUs are viewed as FL participants [set of candidate members], who strive for training a global model collaboratively. All MUs willing to join a specific FL task are denoted by the set N = 123 N.”)
receiving, in response to the providing, first federated learning specific assistance information from the network function; (Section III, A, paragraph 2, “each MU sends a participating-request message to the FL server. This request message consists of their current resource information on their device (e.g., CPU utilization, state of wireless connection) (i.e., wherein assistance information under the broadest reasonable interpretation (BRI) is interpreted as information about the device, which is assistance information)”)
estimating a first expected quality of the intended federated learning training based on the first federated learning specific assistance information; and (Section III, A, paragraph 3, “Assuming that MU i has a number of CPU capacity, which represents its currently available computing resource that can be applied to FL training (i.e., wherein estimating is interpreted as if a device can complete the training task). The network connection quality of each MU is measured by the cellular parameters which can be perceived by each smartphone (i.e., wherein CPU capacity and network connection are interpreted as an expected quality)”)
deciding whether or not to start the intended federated learning training based on the first expected quality of the intended federated learning training (Section III, A, paragraph 4, “the FL server must select an appropriate [deciding whether or not] (i.e., wherein the selecting is interpreted as making a decision) subset of all candidate MUs at the very beginning of an FL training task. A good candidate selection not only can maximize the training efficiency, but also can avoid to wait too long for the slow-training participants, which fail to complete their local training and/or upload the local models before the deadline of a round (i.e., wherein the expected quality is a deciding factor to start the intended federated learning training)”)
Regarding claim 4:
Huang teaches the apparatus of claim 1.
Huang further teaches:
wherein the first federated learning specific assistance information comprises at least one of for each of the candidate members of the set of the candidate members: an expected latency for performing at least one iteration of the local training by the respective candidate member; (Section III, B, paragraph 4, “We call a participant a valid one if it can successfully complete the training and reporting its updated local model to the FL server within a timeslot (i.e., wherein expected latency under the broadest reasonable interpretation (BRI) is the time required for computing, hence successfully complete training is valid). Thus, the objective function (1) claims that the total number of valid participants should be maximized for each FL round t T . Constraint (2) defines the maximum number, i.e., K, of all participants that are selected by FL server for each round of training.”)
for each of the candidate members of the set of the candidate members: an expected average latency for performing plural iterations of the local training by the respective candidate member; (Section VI, A, paragraph 1, “Basic Settings: In experiment settings, we set totally N=100 mobile devices [set of candidate members] as the candidates for federated learning. At most K=10 of them will be selected to participate in each round of federated learning. At the beginning of each round, all candidates report their device information, which includes their individual network connection quality and current computing capability (i.e., wherein expected average latency under the broadest reasonable interpretation (BRI) is interpreted as an estimation of how long a candidate member will take to complete, hence, computing capability), to the FL parameter sever. Next, the parameter server selects the best group of devices as the participants for training an FL task, according to different candidate-selection algorithms. Under the proposed DDQN based algorithm, after a number of E episodes of training [iterations of the local training] with other parameters configured following Table II, the DRL Core will learn a good policy for candidate selection. This good policy is then provided to the FL server to guide the distribution of global FL model.”)
for each of the candidate members of the set of the candidate members: an expected average latency for providing a result of an iteration of the local training to the apparatus; (Section VI, A, paragraph 1, “Basic Settings: In experiment settings, we set totally N=100 mobile devices [set of candidate members] as the candidates for federated learning”…Section VI, E, “Recall that we call a candidate a valid participant if it can successfully report its updated local model to the FL server within a round of FL training (i.e., wherein report its updated local model is interpreted as providing a result to the apparatus)”)
a suggested time window for performing the federated learning training; and (Section III, A, paragraph 2, “This request message consists of their current resource information on their device (e.g., CPU utilization, state of wireless connection). To conveniently describe the execution of FL system, we also call each round of FL training a timeslot [a suggested time window], which is illustrated in Fig. 2. All timeslots are recorded in set T = 123 T . Thus, the deadline for each round of FL training is actually the length of a timeslot [for performing the federated learning]. In a real experimental setting, the length of a timeslot could be measured in milliseconds.”)
a geographical distribution of the candidate members (Section IV, B, “The GPS data, i.e., the latitude and longitude [geographical distribution], of each MU [candidate member], which is used to calculate the Place of Interests (POIs) [10] of candidates.”)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-3, 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., in view of Li et al., (US20220038349A1).
Regarding claim 2:
Huang teaches the apparatus of claim 1.
Huang does not explicitly teach:
wherein at least one of the following is valid: the network function is a stand-alone network function dedicated to assisting the federated learning training; the network function is comprised by a network exposure function; and the network function is comprised by a network data analytics function.
Li teaches:
wherein at least one of the following is valid: the network function is a stand-alone network function dedicated to assisting the federated learning training; the network function is comprised by a network exposure function; and the network function is comprised by a network data analytics function ([0039], “FIG. 1C illustrates a 5G system architecture 140C and a service-based representation. In addition to the network entities illustrated in FIG. 1B, system architecture 140C can also include a network exposure function (NEF) [the network function is comprised by a network exposure function] 154 and a network repository function (NRF) 156. In some aspects, 5G system architectures can be service-based and interaction between network functions [network function] can be represented by corresponding point-to-point reference points N1 or as service-based interfaces”...[0032], “The 5G system architecture 140B includes a plurality of network functions (NFs), such as an AMF 132, session management function (SMF) 136, policy control function (PCF) 148, application function (AF) 150, UPF 134, network slice selection function (NSSF) 142, authentication server function (AUSF)”…[0035], “The AF 150 may provide information on the packet flow to the PCF 148 responsible for policy control to support a desired QoS [a network data analytics function] (i.e., wherein under the broadest reasonable interpretation (BRI) QoS is an analytics use case using NWDAF)”
Li and Huang are both related to the same field of endeavor (i.e., federated learning). In view of the teachings of Li it would have been obvious for a person of ordinary skill in the art to apply the teachings of Li to Huang before the effective filing date of the claimed invention in order to improve the efficiency of training coordination in federated learning in wireless networks (Li, Abstract, “The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.”)
Regarding claim 3:
Huang teaches the apparatus of claim 1.
Huang further teaches:
wherein the information related to the intended federated learning training comprises at least one of: the candidate members; (Section I, paragraph 6, “That is, candidate devices [candidate members] are selected only by their currently-observed resources, such as the network-connection quality and the remaining computing capability.”)
an expected number of iterations to be performed by each of the candidate members when performing the respective local training; for each of the candidate members: a time interval between each of the iterations to be performed by the respective candidate member; and (Section II, B, paragraph 5, “That is, the candidate-selection overall mobile participants is performed only when receiving their current device information”…Section III, A, paragraph 2, “To conveniently describe the execution of FL system, we also call each round of FL training a timeslot, which is illustrated in Fig. 2. All timeslots are recorded in set T = 123 T . Thus, the deadline for each round of FL training is actually the length of a timeslot [a time interval] (i.e., wherein timeslot under the broadest reasonable interpretation (BRI) is interpreted as a timeslot)”)
Huang does not explicitly teach:
a size of an aggregated model of the intended federated learning training;
a size of a local model of at least one of the local trainings;
an identifier of a network slice supporting the apparatus.
Li further teaches:
a size of an aggregated model of the intended federated learning training; ([0058], “The federated learning scheme between the RAN and its connected UEs may include the gNB distributed unit/central unit (gNB-DU/gNB-CU) or location management function (LMF) for positioning acting as a “central server” that is responsible for selecting the training model, training the models, transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model [a size of an aggregated model] (i.e., wherein aggregating parameters under the broadest reasonable interpretation (BRI) is interpreted as the size of the aggregated model)”)
a size of a local model of at least one of the local trainings; ([0058], The UEs act as the “local node”, responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally [local model] with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF (i.e., wherein a model’s parameters under the broadest reasonable interpretation (BRI) is interpreted as ‘a size’))
an identifier of a network slice supporting the apparatus ([0033], “The AMF 132 [an identifier] can be used to manage access control and mobility and can also include network slice selection functionality [a network slice]. The AMF 132 may provide UE-based authentication, authorization, mobility management, etc., and may be independent of the access technologies. The SMF 136 can be configured to set up and manage various sessions according to network policy.”)
The motivation for claim 3 is the same motivation of claim 2.
Regarding claim 5:
Huang teaches the apparatus of claim 1.
Huang does not explicitly teach:
wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform at least one of: starting the intended federated learning training if it is decided to start the intended federated learning training; and inhibiting the starting the intended federated learning training if it is decided not to start the intended federated learning training
Li further teaches:
wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform at least one of: starting the intended federated learning training if it is decided to start the intended federated learning training; and inhibiting the starting the intended federated learning training if it is decided not to start the intended federated learning training ([0065]“Operation 1: The local node (UE) decides which service/model the UE would like to utilize and replies to the RAN with its interested model and its local AI/ML capability. If the UE cannot meet the threshold of its own minimum AUML capability requirement, the UE may skip the AUML service/model request. Additionally, or alternatively, the RAN may select candidate UEs for federated learning. The UE selected may accept or reject the request (i.e., wherein starting/ not starting under the broadest reasonable interpretation (BRI) is interpreted as making a decision ‘accept or reject the request’ to start FL training)”)
The motivation for claim 5 is the same motivation of claim 2.
Regarding claim 6:
Huang, as modified by Li, teaches the apparatus of claim 5.
Huang further teaches:
wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform if it is decided not to start the intended federated learning training: requesting second federated learning specific assistance information for the intended federated learning training under the assumption that at least one further candidate member not belonging to the set of candidate members performs the respective local training; (Section VI, A, paragraph 1, “In experiment settings, we set totally N=100 mobile devices as the candidates [set of candidate members] for federated learning. At most K=10 of them will be selected to participate in each round of federated learning (i.e., wherein under the broadest reasonable interpretation (BRI) in each round is interpreted to be a second etc.). At the beginning of each round, all candidates report their device information, which includes their individual network connection quality and current computing capability (i.e., wherein the quality and capability is part of assistance information), to the FL parameter sever. Next, the parameter server selects the best group of devices as the participants for training an FL task, according to different candidate-selection algorithms (i.e., wherein under the broadest reasonable interpretation (BRI) after the first round, a second round where another candidate is selected to do the training, which was not included in the first round). Under the proposed DDQN based algorithm, after a number of E episodes of training with other parameters configured following Table II, the DRL Core will learn a good policy for candidate selection. This good policy is then provided to the FL server to guide the distribution of global FL model.”)
receiving, in response to the providing, the second federated learning specific assistance information from the network function; (Section III, A, paragraph 2, “each MU sends a participating-request message to the FL server. This request message consists of their current resource information on their device (e.g., CPU utilization, state of wireless connection) (i.e., wherein assistance information under the broadest reasonable interpretation (BRI) is interpreted as information about the device, also done for a second round)”)
estimating a second expected quality of the intended federated learning training based on the second federated learning specific assistance information; (Section III, A, paragraph 3, “Assuming that MU i has a number of CPU capacity, which represents its currently available computing resource that can be applied to FL training (i.e., wherein estimating is interpreted as if a device can complete the training task, also done for a second round). The network connection quality of each MU is measured by the cellular parameters which can be perceived by each smartphone (i.e., wherein CPU capacity and network connection are interpreted as an expected quality, which is assistance information)”)
deciding whether or not to start the intended federated learning training based on the second expected quality of the intended federated learning training; and starting the intended federated learning training if it is decided to start the intended federated learning training (Section III, A, paragraph 4, “the FL server must select an appropriate [deciding whether or not] (i.e., wherein the selecting is interpreted as making a decision) subset of all candidate MUs at the very beginning of an FL training task (i.e., wherein under the broadest reasonable interpretation (BRI), if yes to start, then the intended training starts). A good candidate selection not only can maximize the training efficiency, but also can avoid to wait too long for the slow-training participants, which fail to complete their local training and/or upload the local models before the deadline of a round (i.e., wherein the expected quality is a deciding factor to start the intended federated learning training, also done for a second round)”)
The motivation for claim 6 is the same motivation of claim 2.
Claim(s) 7-8, 10, 16-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., in view of Akdeniz et al., (US20230068386A1)
Regarding claim 7 and analogous claim 16:
Huang teaches:
one or more processors, and at least one memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform: receiving information related to an intended federated learning training, wherein the intended federated learning training involves a set of candidate members performing a respective local training; (Section III, A, paragraph 1, “As illustrated in Fig. 1, we consider a mobile edge computing (MEC) environment, which is built on metropolitan cellular networks. The synchronous-fashioned computing architecture consists of a central FL parameter server, a group of base stations, and a large number of mobile devices with heterogeneous resources. The central FL parameter server initializes a global model for a specific computing task [intended federated learning] (i.e., wherein computing task under the broadest reasonable interpretation (BRI) is an intended task (i.e., FL training)) such as image classification (i.e., wherein training task). For each training task, the FL server recruits a group of mobile users (MUs) to join in a training task. The selected MUs are viewed as FL participants [set of candidate members], who strive for training a global model collaboratively. All MUs willing to join a specific FL task are denoted by the set N = 123 N.”)
providing the federated learning specific assistance information in response to the receiving the information related to the intended federated learning training (Section III, A, paragraph 3, “Assuming that MU i has a number of CPU capacity, which represents its currently available computing resource that can be applied to FL training (i.e., wherein providing to a device to complete the training). The network connection quality of each MU is measured by the cellular parameters which can be perceived by each smartphone (i.e., wherein parameters are interpreted as assistance information)”)
Huang does not explicitly teach:
generating federated learning specific assistance information for the intended federated learning training based on the information related to the intended federated learning training;
Akdeniz teaches:
generating federated learning specific assistance information for the intended federated learning training based on the information related to the intended federated learning training; ([0068], “while the processing of data that is of a higher urgency or importance may be performed by the edge gateway devices 620 (depending on, for example, the capabilities of each component, or information in the request indicating urgency or importance). Based on data access, data location or latency, (i.e., wherein under the broadest reasonable interpretation (BRI) the assistance information is based on information related to the training) work may continue on edge resource nodes when the processing priorities change during the processing activity. Likewise, configurable systems or hardware resources themselves can be activated (e.g., through a local orchestrator) to provide additional resources to meet the new demand (e.g., adapt the compute resources to the workload data).”)
Akdeniz and Huang are both related to the same field of endeavor (i.e., federated learning). In view of the teachings of Akdeniz it would have been obvious for a person of ordinary skill in the art to apply the teachings of Akdeniz to Huang before the effective filing date of the claimed invention in order to improve the efficiency of training in federated learning in wireless networks (Akdeniz, [0264], “the MEC server may request the clients to share their respective compute rates and communication times in order to estimate the total update time from each client. Based on the above, the MEC server may perform a client set selection procedure by grouping the clients into sets for each training round.”)
Regarding claim 8 and analogous claim 17:
Huang, as modifies by Akdeniz, teaches the apparatus of claim 7.
Huang further teaches:
wherein the generating the federated learning specific assistance information comprises at least one of: for each of the candidate members, checking whether there is a consent to perform the respective local training; (Section III, A, paragraph 2, “each MU [set of candidate members] sends a participating-request [consent to perform] message to the FL server (i.e., wherein a device sends a request under the broadest reasonable interpretation (BRI) is interpreted to provide consent at the time the request was sent). This request message consists of their current resource information on their device (e.g., CPU utilization, state of wireless connection) (i.e., wherein assistance information under the broadest reasonable interpretation (BRI) is interpreted as information about the device)”)
for each of the candidate members, obtaining a current location of the respective candidate member; (Section IV, B, “The GPS data, i.e., the latitude and longitude (i.e., wherein current location is interpreted as GPS data), of each MU [candidate member], which is used to calculate the Place of Interests (POIs) [10] of candidates.”)
for each of the candidate members, estimating a future location of the respective candidate member for the time when the respective local training is to be performed; for each of the candidate members, a respective data capacity for performing the respective local training and/or for transmitting a result of the respective local training; for each of the candidate members, estimating a radio link quality at the current location of the respective candidate member and/or at the future location of the respective candidate member; for each of the candidate members, checking if a slice supports the current location of the respective candidate member and/or the future location of the respective candidate member, wherein the information related to the intended federated learning training comprises an identifier of the slice; and for each of the candidate members, checking if the slice supports the respective candidate member.
The motivation for claim 8 is the same motivation of claim 7.
Regarding claim 10 and analogous claim 19:
Huang, as modifies by Akdeniz, teaches the apparatus of claim 7.
Huang further teaches:
wherein the federated learning specific assistance information comprises at least one of for each of the candidate members of the set of the candidate members: an expected latency for performing at least one iteration of the local training by the respective candidate member; (Section III, B, paragraph 4, “We call a participant a valid one if it can successfully complete the training and reporting its updated local model to the FL server within a timeslot (i.e., wherein expected latency under the broadest reasonable interpretation (BRI) is the time required for computing, hence successfully complete training is valid). Thus, the objective function (1) claims that the total number of valid participants should be maximized for each FL round t T . Constraint (2) defines the maximum number, i.e., K, of all participants that are selected by FL server for each round of training.”)
for each of the candidate members of the set of the candidate members: an expected average latency for performing plural iterations of the local training by the respective candidate member; (Section VI, A, paragraph 1, “Basic Settings: In experiment settings, we set totally N=100 mobile devices [set of candidate members] as the candidates for federated learning. At most K=10 of them will be selected to participate in each round of federated learning. At the beginning of each round, all candidates report their device information, which includes their individual network connection quality and current computing capability (i.e., wherein expected average latency under the broadest reasonable interpretation (BRI) is interpreted as an estimation of how long a candidate member will take to complete, hence, computing capability), to the FL parameter sever. Next, the parameter server selects the best group of devices as the participants for training an FL task, according to different candidate-selection algorithms. Under the proposed DDQN based algorithm, after a number of E episodes of training [iterations of the local training] with other parameters configured following Table II, the DRL Core will learn a good policy for candidate selection. This good policy is then provided to the FL server to guide the distribution of global FL model.”)
for each of the candidate members of the set of the candidate members: an expected average latency for providing a result of an iteration of the local training to the apparatus; (Section VI, A, paragraph 1, “Basic Settings: In experiment settings, we set totally N=100 mobile devices [set of candidate members] as the candidates for federated learning”…Section VI, E, “Recall that we call a candidate a valid participant if it can successfully report its updated local model to the FL server within a round of FL training (i.e., wherein report its updated local model is interpreted as providing a result to the apparatus)”)
a suggested time window for performing the federated learning training; and (Section III, A, paragraph 2, “This request message consists of their current resource information on their device (e.g., CPU utilization, state of wireless connection). To conveniently describe the execution of FL system, we also call each round of FL training a timeslot [a suggested time window], which is illustrated in Fig. 2. All timeslots are recorded in set T = 123 T . Thus, the deadline for each round of FL training is actually the length of a timeslot [for performing the federated learning]. In a real experimental setting, the length of a timeslot could be measured in milliseconds.”)
a geographical distribution of the candidate members (Section IV, B, “The GPS data, i.e., the latitude and longitude [geographical distribution], of each MU [candidate member], which is used to calculate the Place of Interests (POIs) [10] of candidates.”)
The motivation for claim 10 is the same motivation of claim 7.
Claim(s) 9, 11, 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., in view of Akdeniz et al., and further in view of Li et al.,
Regarding claim 9 and analogous claim 18:
Huang, as modifies by Akdeniz, teaches the apparatus of claim 7.
Huang further teaches:
wherein the information related to the intended federated learning training comprises at least one of: the candidate members of the set of candidate members; (Section I, paragraph 6, “That is, candidate devices [candidate members] are selected only by their currently-observed resources, such as the network-connection quality and the remaining computing capability.”)
an expected number of iterations to be performed by each of the candidate members when performing the respective local training; for each of the candidate members: a time interval between each of the iterations to be performed by the respective candidate member; and (Section II, B, paragraph 5, “That is, the candidate-selection overall mobile participants is performed only when receiving their current device information”…Section III, A, paragraph 2, “To conveniently describe the execution of FL system, we also call each round of FL training a timeslot, which is illustrated in Fig. 2. All timeslots are recorded in set T = 123 T . Thus, the deadline for each round of FL training is actually the length of a timeslot [a time interval] (i.e., wherein timeslot under the broadest reasonable interpretation (BRI) is interpreted as a timeslot)”)
Huang does not explicitly teach:
a size of an aggregated model of the intended federated learning training;
a size of a local model of at least one of the local trainings;
an identifier of a network slice
Li teaches:
a size of an aggregated model of the intended federated learning training; ([0058], “The federated learning scheme between the RAN and its connected UEs may include the gNB distributed unit/central unit (gNB-DU/gNB-CU) or location management function (LMF) for positioning acting as a “central server” that is responsible for selecting the training model, training the models, transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model [a size of an aggregated model] (i.e., wherein aggregating parameters under the broadest reasonable interpretation (BRI) is interpreted as the size of the aggregated model)”)
a size of a local model of at least one of the local trainings; ([0058], The UEs act as the “local node”, responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally [local model] with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF (i.e., wherein a model’s parameters under the broadest reasonable interpretation (BRI) is interpreted as ‘a size’))
an identifier of a network slice ([0033], “The AMF 132 [an identifier] can be used to manage access control and mobility and can also include network slice selection functionality [a network slice]. The AMF 132 may provide UE-based authentication, authorization, mobility management, etc., and may be independent of the access technologies. The SMF 136 can be configured to set up and manage various sessions according to network policy.”)
Li and Huang are both related to the same field of endeavor (i.e., federated learning). In view of the teachings of Li it would have been obvious for a person of ordinary skill in the art to apply the teachings of Li to Huang before the effective filing date of the claimed invention in order to improve the efficiency of training coordination in federated learning in wireless networks (Li, Abstract, “The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.”)
Regarding claim 11 and analogous claim 20:
Huang, as modifies by Akdeniz, teaches the apparatus of claim 7.
Huang does not explicitly teach:
wherein at least one of the apparatus is comprised in a stand-alone network function dedicated to assisting the federated learning training; and the apparatus is comprised in a network data analytics function
Li further teaches:
wherein at least one of the apparatus is comprised in a stand-alone network function dedicated to assisting the federated learning training; and the apparatus is comprised in a network data analytics function ([0039], “FIG. 1C illustrates a 5G system architecture 140C and a service-based representation. In addition to the network entities illustrated in FIG. 1B, system architecture 140C can also include a network exposure function (NEF) 154 and a network repository function (NRF) 156. In some aspects, 5G system [stand-alone network function] architectures can be service-based and interaction between network functions can be represented by corresponding point-to-point reference points N1 or as service-based interfaces”...[0032], “The 5G system architecture 140B includes a plurality of network functions (NFs), such as an AMF 132, session management function (SMF) 136, policy control function (PCF) 148, application function (AF) 150, UPF 134, network slice selection function (NSSF) 142, authentication server function (AUSF)”…[0035], “The AF 150 may provide information on the packet flow to the PCF 148 responsible for policy control to support a desired QoS [a network data analytics function] (i.e., wherein under the broadest reasonable interpretation (BRI) QoS is an analytics use case using NWDAF)”
The motivation for claim 11 is the same motivation of claim 9.
Claim(s) 12, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al., and Akdeniz et al.
Regarding claim 12:
Li teaches:
one or more processors, and at least one memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform: receiving, by a first network function from an application function, information related to an intended federated learning training; instructing the first network function to provide the information related to the intended federated learning training to a second network function; ([0035], “The AF 150 [application function] may provide information on the packet flow [information related to an intended federated learning training] to the PCF 148 responsible for policy control to support a desired QoS. The PCF 148 [first network function] may set mobility and session management policies for the UE 101. To this end, the PCF 148 may use the packet flow information to determine the appropriate policies for proper operation of the AMF 132 and SMF 136 [second network function]. The AUSF 144 may store data for UE authentication”)
receiving, by the first network function, in response to the providing the information related to the intended federated learning training, federated learning specific assistance information from the second network function; and ([0038], “A reference point representation shows that interaction can exist between corresponding NF services (i.e., wherein an interaction is interpreted as receiving and in response to the information shared). For example, FIG. 1B illustrates the following reference points: N1 (between the UE 102 and the AMF 132), N2 (between the RAN 110 and the AMF 132). In addition to the network entities illustrated in FIG. 1B, system architecture 140C can also include a network exposure function (NEF) 154 and a network repository function (NRF) 156. In some aspects, 5G system architectures can be service-based and interaction between network functions (i.e., wherein interaction between network functions under the broadest reasonable interpretation (BRI) is interpreted as first and second network functions) can be represented by corresponding point-to-point reference points N1 or as service-based interfaces.”)
Li does not explicitly teach:
commanding the first network function to forward the federated learning specific assistance information to the application function.
Akdeniz teaches:
commanding the first network function to forward the federated learning specific assistance information to the application function ([0130], “Further, a client (or client compute node) as described herein may refer to an edge compute node that is served, controlled, or otherwise commanded [commanding] by one or more other edge compute nodes (e.g., central server(s) as described above). For instance, as described herein, the clients perform machine learning based on information and/or commands from another node(s) (i.e., wherein specific assistance information) (i.e., a central server(s)). A client device may include a server device, such as a device structurally configured as described herein (e.g., to fit within a server rack or sled), a mobile computing device (e.g., tablet, smartphone, etc.), or may include another type of computing device.”)
Akdeniz and Li are both related to the same field of endeavor (i.e., federated learning). In view of the teachings of Akdeniz it would have been obvious for a person of ordinary skill in the art to apply the teachings of Akdeniz to Li before the effective filing date of the claimed invention in order to improve the efficiency of training in federated learning in wireless networks (Akdeniz, [0264], “the MEC server may request the clients to share their respective compute rates and communication times in order to estimate the total update time from each client. Based on the above, the MEC server may perform a client set selection procedure by grouping the clients into sets for each training round.”)
Regarding claim 15:
Li, as modifies by Akdeniz, teaches the apparatus of claim 12.
Li further teaches:
wherein the first network function is a network exposure function ([0039], “FIG. 1C illustrates a 5G system architecture 140C and a service-based representation. In addition to the network entities illustrated in FIG. 1B, system architecture 140C can also include a network exposure function (NEF) [the network function is comprised by a network exposure function] 154 and a network repository function (NRF) 156. In some aspects, 5G system architectures can be service-based and interaction between network functions [network function] can be represented by corresponding point-to-point reference points N1 or as service-based interfaces”
The motivation for claim 15 is the same motivation of claim 12.
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al., and Akdeniz et al., further in view of Huang et al.
Regarding claim 13:
Li, as modifies by Akdeniz, teaches the apparatus of claim 12.
Li further teaches:
a size of an aggregated model of the intended federated learning training; ([0058], “The federated learning scheme between the RAN and its connected UEs may include the gNB distributed unit/central unit (gNB-DU/gNB-CU) or location management function (LMF) for positioning acting as a “central server” that is responsible for selecting the training model, training the models, transmitting the model to the UEs (local nodes in federated learning), and aggregating parameters from local nodes and updating the trained model [a size of an aggregated model] (i.e., wherein aggregating parameters under the broadest reasonable interpretation (BRI) is interpreted as the size of the aggregated model)”)
a size of a local model of at least one of the local trainings; ([0058], The UEs act as the “local node”, responsible for sending a training model request, receiving a trained model from the RAN (central server in federated learning), training the model locally [local model] with its own data and reporting updated parameters to the gNB-DU/gNB-CU or LMF (i.e., wherein a model’s parameters under the broadest reasonable interpretation (BRI) is interpreted as ‘a size’))
an identifier of a network slice ([0033], “The AMF 132 [an identifier] can be used to manage access control and mobility and can also include network slice selection functionality [a network slice]. The AMF 132 may provide UE-based authentication, authorization, mobility management, etc., and may be independent of the access technologies. The SMF 136 can be configured to set up and manage various sessions according to network policy.”)
Li, as modified by Akdeniz does not explicitly teach:
wherein the information related to the intended federated learning training comprises at least one of: the candidate members of the set of candidate members;
an expected number of iterations to be performed by each of the candidate members when performing the respective local training; for each of the candidate members: a time interval between each of the iterations to be performed by the respective candidate member; and
Huang teaches:
wherein the information related to the intended federated learning training comprises at least one of: the candidate members of the set of candidate members; (Section I, paragraph 6, “That is, candidate devices [candidate members] are selected only by their currently-observed resources, such as the network-connection quality and the remaining computing capability.”)
an expected number of iterations to be performed by each of the candidate members when performing the respective local training; for each of the candidate members: a time interval between each of the iterations to be performed by the respective candidate member; and (Section II, B, paragraph 5, “That is, the candidate-selection overall mobile participants is performed only when receiving their current device information”…Section III, A, paragraph 2, “To conveniently describe the execution of FL system, we also call each round of FL training a timeslot, which is illustrated in Fig. 2. All timeslots are recorded in set T = 123 T . Thus, the deadline for each round of FL training is actually the length of a timeslot [a time interval] (i.e., wherein timeslot under the broadest reasonable interpretation (BRI) is interpreted as a timeslot)”)
Huang and Li are both related to the same field of endeavor (i.e., federated learning). In view of the teachings of Huang it would have been obvious for a person of ordinary skill in the art to apply the teachings of Huang to Li before the effective filing date of the claimed invention in order to improve the efficiency of candidate selection for training in federated learning in wireless networks (Huang, Abstract,“ Federated Learning (FL) is viewed as a promising technique for future distributed machine learning. It permits a large number of mobile devices participating in the training of a global model collaboratively without having to expose their local private data. Although the challenge of the network connection will be much relieved in 5G/B5G era, the training latency is still an obstacle preventing FL from being largely adopted. One of the most fundamental problems that leads to large training latency is the bad candidate-selection of FL participants.”)
Regarding claim 14:
Li, as modifies by Akdeniz, teaches the apparatus of claim 12.
Li, as modifies by Akdeniz does not explicitly teach:
wherein the federated learning specific assistance information comprises at least one of for each of the candidate members of the set of the candidate members: an expected latency for the local training to be performed by the respective candidate member; a suggested time window for performing the local trainings by the candidate members; and a geographical distribution of the candidate members.
Huang further teaches:
wherein the federated learning specific assistance information comprises at least one of for each of the candidate members of the set of the candidate members: an expected latency for the local training to be performed by the respective candidate member; (Section VI, A, paragraph 1, “Basic Settings: In experiment settings, we set totally N=100 mobile devices [set of candidate members] as the candidates for federated learning. At most K=10 of them will be selected to participate in each round of federated learning. At the beginning of each round, all candidates report their device information, which includes their individual network connection quality and current computing capability (i.e., wherein expected average latency under the broadest reasonable interpretation (BRI) is interpreted as an estimation of how long a candidate member will take to complete, hence, computing capability), to the FL parameter sever. Next, the parameter server selects the best group of devices as the participants for training an FL task, according to different candidate-selection algorithms. Under the proposed DDQN based algorithm, after a number of E episodes of training [local training] with other parameters configured following Table II, the DRL Core will learn a good policy for candidate selection. This good policy is then provided to the FL server to guide the distribution of global FL model.”)
a suggested time window for performing the local trainings by the candidate members; and (Section III, A, paragraph 2, “This request message consists of their current resource information on their device (e.g., CPU utilization, state of wireless connection). To conveniently describe the execution of FL system, we also call each round of FL training a timeslot [a suggested time window], which is illustrated in Fig. 2. All timeslots are recorded in set T = 123 T . Thus, the deadline for each round of FL training is actually the length of a timeslot [for performing the federated learning]. In a real experimental setting, the length of a timeslot could be measured in milliseconds.”)
a geographical distribution of the candidate members (Section IV, B, “The GPS data, i.e., the latitude and longitude [geographical distribution], of each MU [candidate member], which is used to calculate the Place of Interests (POIs) [10] of candidates.”)
The motivation for claim 14 is the same motivation of claim 13.
Conclusion
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/AMINA MORENO BENOURAIDA/ Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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